Musical similarity

The notion of musical similarity is particularly complex because there are numerous dimensions of similarity. If similarity takes place between different fragments from one musical piece, a musical similarity implies a repetition of the first occurring fragment. As well, eventually, the similarity does not occur by direct repetition, but by presenting in two (or more) set of relations, some common values or patterns. Objective musical similarity can be based on musical features such as:

Pitched parameters

Pitch interval similarity
Melodic similarity[1]
Modulation pattern similarity
Timbral similarity

Non-pitched parameters

Metrical structure similarity
Rhythmic pattern similarity[2]
Section structure similarity

Semiotic parameters

Modality structure similarity
Extensional similarity
Intensional similarity

Nevertheless, similarity can be based also on less objective features such as musical genre, personal history, social context (e.g. music from the 1960s), and a priori knowledge.

Similarity is relevant also in music information retrieval.[3] Finally, musical similarity can be extended to the comparison between musical gestures in performance and composition.[4]

Applications

Automatic methods of musical similarity detection based on data mining and co-occurrence analysis has been developed in order to classify music titles for electronic music distribution.[5]

References

Greg Aloupis, Thomas Fevens, Stefan Langerman, Tomomi Matsui, Antonio Mesa, Yurai Nunez, and David Rappaport, and Godfried T. Toussaint, "Algorithms for computing geometric measures of melodic similarity," Computer Music Journal, Vol. 30, No. 3, Fall 2006, pp. 67–76.
Godfried T. Toussaint, "A comparison of rhythmic dissimilarity measures," FORMA, Vol. 21, No. 2, 2006, pp. 129–149.
Arshia Cont, Shlomo Dubnov, Gérard Assayag. On the Information Geometry of Audio Streams with Applications to Similarity Computing. IEEE Transactions on Audio, Speech, and Language Processing, Institute of Electrical and Electronics Engineers, 2011, 19 (4), pp. 837-846.
Maria Mannone, Introduction to gestural similarity in music. An application of category theory to the orchestra, Journal of Mathematics and Music, Vol. 12, No. 2, 2018, pp. 63–85.
François Pachet, Geert Westermann, Damien Laigre, Musical Data Mining for Electronic Music Distribution Archived 2014-03-27 at the Wayback Machine. Proceedings of the 1st WedelMusic Conference, pp. 101-106, Firenze, Italy, 2001.

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[1] Greg Aloupis, Thomas Fevens, Stefan Langerman, Tomomi Matsui, Antonio Mesa, Yurai Nunez, and David Rappaport, and Godfried T. Toussaint, "Algorithms for computing geometric measures of melodic similarity," Computer Music Journal, Vol. 30, No. 3, Fall 2006, pp. 67–76.

[2] Godfried T. Toussaint, "A comparison of rhythmic dissimilarity measures," FORMA, Vol. 21, No. 2, 2006, pp. 129–149.

[3] Arshia Cont, Shlomo Dubnov, Gérard Assayag. On the Information Geometry of Audio Streams with Applications to Similarity Computing. IEEE Transactions on Audio, Speech, and Language Processing, Institute of Electrical and Electronics Engineers, 2011, 19 (4), pp. 837-846.

[4] Maria Mannone, Introduction to gestural similarity in music. An application of category theory to the orchestra, Journal of Mathematics and Music, Vol. 12, No. 2, 2018, pp. 63–85.

[5] François Pachet, Geert Westermann, Damien Laigre, Musical Data Mining for Electronic Music Distribution Archived 2014-03-27 at the Wayback Machine. Proceedings of the 1st WedelMusic Conference, pp. 101-106, Firenze, Italy, 2001.